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Varando, Gherardo and Benavides Piccione, Ruth and Muñoz Cespedes, Alberto and Kastanauskaite, Asta and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María and De Felipe Oroquieta, Javier (2018). MultiMap: a tool to automatically extract and analyse spatial microscopic data from large stacks of confocal microscopy images. "Frontiers in Neuroanatomy", v. 12 ; pp. 1-12. ISSN 1662-5129. https://doi.org/10.3389/fnana.2018.00037.
Title: | MultiMap: a tool to automatically extract and analyse spatial microscopic data from large stacks of confocal microscopy images |
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Author/s: |
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Item Type: | Article |
Título de Revista/Publicación: | Frontiers in Neuroanatomy |
Date: | May 2018 |
ISSN: | 1662-5129 |
Volume: | 12 |
Subjects: | |
Freetext Keywords: | Segmentation; Object detection; Fluorescent image; Puncta segmentation; Vglut1; Vgat; Brain atlas; ImageJ |
Faculty: | E.T.S. de Ingenieros Informáticos (UPM) |
Department: | Inteligencia Artificial |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
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The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections.
Type | Code | Acronym | Leader | Title |
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Government of Spain | C080020-09 | Unspecified | Unspecified | Cajal Blue Brain Project |
Government of Spain | TIN2016-79684-P | Unspecified | Universidad Politécnica de Madrid | Avances en clasificación multidimensional y detección de anomalías con redes bayesianas |
Horizon 2020 | 785907 | HBP SGA2 | Unspecified | Human Brain Project Specific Grant Agreement 2 |
Madrid Regional Government | S2013/ICE-2845 | CASI – CAM | Unspecified | Conceptos y aplicaciones de los sistemas inteligentes |
Item ID: | 54546 |
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DC Identifier: | http://oa.upm.es/54546/ |
OAI Identifier: | oai:oa.upm.es:54546 |
DOI: | 10.3389/fnana.2018.00037 |
Official URL: | https://www.frontiersin.org/articles/10.3389/fnana.2018.00037/full |
Deposited by: | Memoria Investigacion |
Deposited on: | 16 May 2019 07:24 |
Last Modified: | 16 May 2019 07:24 |